class_docstr: Logistic Regression classifier.
  Parameters:
  -----------
  learning_rate: float
      The step length that will be taken when following the negative gradient during
      training.
  gradient_descent: boolean
      True or false depending if gradient descent should be used when training. If
      false then we use batch optimization by least squares.
code: def _initialize_parameters(self, X):
      n_features = np.shape(X)[1]
      # Initialize parameters between [-1/sqrt(N), 1/sqrt(N)]
      limit = 1 / math.sqrt(n_features)
      self.param = np.random.uniform(-limit, limit, (n_features,))
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/logistic_regression.py
imports: from __future__ import print_function, division
  import numpy
  import math
  from mlfromscratch.utils import make_diagonal, Plot
  from mlfromscratch.deep_learning.activation_functions import Sigmoid
label_desc: Use image node_id 2 for calling the LogisticRegression obj's underlying member method code with example usage: obj._initialize_parameters(X) without return types